Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition

Tang, Jingwei; Chien, Ying-Ren

doi:10.3390/s22197414

Cited by 13 publications

(4 citation statements)

References 28 publications

(34 reference statements)

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“…Liu Hui et al [39] converted traffic flow data into different frequency components using VMD, optimized weights using ICA algorithm, and integrated three prediction models (GMDH, BILSTM, and ELMAN) to predict different modal components. Tang Jingwei et al [40] decomposed environmental variables using VMD and found the most power-relevant modal components based on MIC and Pearson correlation coefficients, predicting them along with historical power data. Cai Changchun et al [41] utilized GRU models to achieve prediction of low frequency components and TCN models to achieve prediction of high frequency components after VMD modal decomposition.…”

Section: Related Workmentioning

confidence: 99%

A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

Guo

Liu

et al. 2023

Applied Sciences

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Traffic flow forecasting, as an integral part of intelligent transportation systems, plays a critical part in traffic planning. Previous studies have primarily focused on short-term traffic flow prediction, paying insufficient attention to long-term prediction. In this study, we propose a hybrid model that utilizes variational mode decomposition (VMD) and the auto-correlation mechanism for long-term prediction. In view of the periodic and stochastic characteristics of traffic flow, VMD is able to decompose the data into intrinsic mode functions with different frequencies, which in turn helps the model extract the internal features of the data and better capture the changes of traffic flow data in the cycle. Additionally, we improve the residual structure by adding a convolutional layer to propose a correction module and use it together with the auto-correlation mechanism to jointly build an encoder and decoder to extract features from different data components (intrinsic mode functions) and fuse the extracted features for output. To meet the requirements of long-term forecasting, we set the traffic flow forecast length to 4 levels: 96, 192, 336, and 720. We validated our model using the departure statistics dataset of a taxi parking lot at Beijing Capital International Airport and achieved the best prediction performance in terms of mean squared error and mean absolute error, compared to the baseline model.

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Section: Related Workmentioning

confidence: 99%

A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

Guo

Liu

et al. 2023

Applied Sciences

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“…TCN is a convolutional neural network specifically designed for analyzing time series data [23][24][25]. It consists of three key components: causal convolution, dilated convolution, and residual connectivity.…”

Section: Time Convolutional Networkmentioning

confidence: 99%

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

Zhang,

Sun

et al. 2023

Applied Sciences

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Wind power generation is a renewable energy source, and its power output is influenced by multiple factors such as wind speed, direction, meteorological conditions, and the characteristics of wind turbines. Therefore, accurately predicting wind power is crucial for the grid operation and maintenance management of wind power plants. This paper proposes a hybrid model to improve the accuracy of wind power prediction. Accurate wind power forecasting is critical for the safe operation of power systems. To improve the accuracy of wind power prediction, this paper proposes a hybrid model incorporating variational modal decomposition (VMD), a Sparrow Search Algorithm (SSA), and a temporal-convolutional-network-based bi-directional gated recurrent unit (TCN-BiGRU). The model first uses VMD to break down the raw power data into several modal components, and then it builds an SSA-TCN-BIGRU model for each component for prediction, and finally, it accumulates all the predicted components to obtain the wind power prediction results. The proposed short-term wind power prediction model was validated using measured data from a wind farm in China. The proposed VMD-SSA-TCN-BiGRU forecasting framework is compared with benchmark models to verify its practicability and reliability. Compared with the TCN-BiGRU, the symmetric mean absolute percentage error, the mean absolute error, and the root mean square error of the VMD-SSA-TCN-BiGRU model reduced by 34.36%, 49.14%, and 55.94%.

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“…Most of these papers proposed models for locations in China, such as [21][22][23][24][25][26][27][28]. Six works proposed models for locations in the USA [29][30][31][32][33][34] and nine works for locations in Europe, such as Spain and Belgium [35][36][37][38][39][40][41][42][43]. Only one work was found that targeted the hot desert climate in which data from a wind farm in Pakistan was used [44].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate

Alkhayat,

Hasan,

Mehmood

2023

Sustainability

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Advancements in technology, policies, and cost reductions have led to rapid growth in wind power (WP) production. One of the major challenges in wind energy production is the instability of WP generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning (DL) are delivering competitive performance versus traditional methods, like numerical weather prediction (NWP), statistical models and machine learning (ML) models. This is truer for short-term prediction. Since there is a relationship between methods, climates and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper presents a novel model that combines the variational mode decomposition (VMD) method with a long short-term memory (LSTM) model for next-hour wind speed (WS) prediction in a hot desert climate, such as the climate in Saudi Arabia. The proposed model performance is compared to two other hybrid models, six DL models and four ML models using different feature sets. Also, the proposed model is tested on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill (FS) between 61% and 74% based on mean absolute error (MAE), 64% and 72% based on root mean square error (RMSE), and 59% and 68% based on mean absolute percentage error (MAPE) for locations in Saudi Arabia.

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Research on Wind Power Short-Term Forecasting Method Based on Temporal Convolutional Neural Network and Variational Modal Decomposition

Cited by 13 publications

References 28 publications

A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

A Long-Term Traffic Flow Prediction Model Based on Variational Mode Decomposition and Auto-Correlation Mechanism

Short-Term Wind Power Forecasting Based on VMD and a Hybrid SSA-TCN-BiGRU Network

A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate

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